The research in Artificial Intelligence is rapidly productized and there are plenty of online solutions available at your disposal. Some of the Computer Vision related tasks (like segmentation, or image classification) give you an acceptable accuracy without training or customizing the models. However, if your data is a bit specific, close-ups, medical images, or product images, you will find fine-tuned models to work much better.

If you work with text then you probably noticed that such models do not generalize well across different styles of documents. To give you a concrete example our recent work on sentiment analysis for German proved how significant the custom approach is to achieve good performance. The table below summarises results on sentiment analysis tasks of GermEval (2017), the most popular German NLP competition.

Solution

Task 1a

Task 1b

Cloud

0.654

0.671

Sayyed et al. (2017)

0.733

0.750

Naderalvojoud et al. (2017)

0.749

0.736

Ours

0.765

0.781

While we’re proud of getting the best accuracy in both tasks, it is much more important to notice how poorly the generic solution performed compared with the custom models.

Some of our European customers prefer to keep their data in-house, governed by their security policies and teams. We can adapt to your requirements, and use your in-house GPU servers or our company workstations all within your corporate VPN.

Once the models are ready, we are going to help your system administrators to deploy them safely in your environment by providing Docker containers or automated recipes (puppet, ansible).

There are many efficient algorithms for Natural Language Processing (NLP), but most of them focus on English. Other languages often do not get the same attention. We push further research on NLP for commonly used languages, including languages morphologically richer than English. Our solutions for Polish and German have already shown state-of-the-art performance. We’re eager to help you with your language of choice.

Whether you collect your data for years or just started, recent advancement in deep learning can help you get the best performance. There are two popular approaches to solve the small data problem.

Transfer learning technique allows us to leverage existing open datasets that are similar to the problem you're trying to solve to pretrain models that later can be fine-tuned for your particular data. This approach reduced the data needs even by a 1000 fold in case of images and recent research (Jan 2018) shows that it can reduce data needs by 100 times in case of Natural Language Processing (NLP).

GANs (Generative Adversary Networks) - this fancy named technique let us convert one set of images with labels into another one that better represents real-world examples. Think converting screenshots from documents into photos of such documents, Thanks to this technology iPhone X with FaceID is able to tell whether your eyes are looking at the screen or not. This kind of technique is best used with images but the attempts to apply it to text generation have limited success.